Search Results (1,267)

Driving fatigue is the cause of many traffic accidents and poses a serious threat to road safety. To address this issue, this paper aims to develop a system for the early detection of driver fatigue. The system leverages heart rate variability (HRV) features and embedded machine learning to estimate the driver’s fatigue level. The driver’s HRV is derived from electrocardiogram (ECG) signals captured by a wearable device for analysis. Time- and frequency-domain HRV features are then extracted and used as the input for a machine learning classifier. A dataset of HRV features is collected from a driving simulation experiment involving 18 participants. Four machine learning classifiers are evaluated, and a backpropagation neural network (BPNN) is selected for its superior performance, achieving up to 94.35% accuracy. The optimized classifier is successfully deployed on an embedded system, providing a cost-effective and portable solution for the early detection of driver fatigue. The results demonstrate the feasibility of using HRV-based machine learning models for the early detection of driver fatigue, contributing to enhanced road safety and a reduced accident risk. Full article

Background/Objectives: QTc prolongation can result in lethal arrhythmia. Risk scores like the Tisdale score can be used for risk stratification for targeted pharmaceutical interventions. However, the practical usability across different medical specialties has not been sufficiently investigated. The aim of this study was to compare relevant risk factors for QTc prolongation and to investigate the use of the Tisdale score in cardiology and gastroenterology patients. Methods: For patients on a cardiology and a gastroenterology ward receiving a weekly pharmaceutical electronic chart review, risk factors for QTc prolongation, QTc-prolonging drugs, and electrocardiograms (ECGs) were retrospectively collected for a four-month period (07-10/2023), and the Tisdale score and its sensitivity and specificity were calculated. Results: A total of 627 chart reviews (cases) (335 cardiology, 292 gastroenterology) were performed. The median age was 66 (range 20–94) years, and 39% (245) of patients were female. The presence of established risk factors (hypokalemia, renal impairment, age ≥ 68 years, cardiac diseases) differed significantly between the specialties. A median of 2 (range 0–5) QTc-prolonging drugs were prescribed in both groups. Baseline and follow-up ECG were recorded in 166 (50%) cardiology cases, of which prolonged QTc intervals were detected in 38 (23%) cases. In the 27 (9%) gastroenterology cases with baseline and follow-up ECG, no QTc prolongations were detected. Across both specialties, the Tisdale score achieved a sensitivity of 74% and a specificity of 30%. Conclusions: The presence of established risk factors for QTc prolongation differed significantly between cardiology and gastroenterology cases. The Tisdale score showed acceptable sensitivity for risk stratification; however, the limited availability of ECGs for gastroenterology cases was a limiting factor. Full article

Supraventricular arrhythmias (SVAs), including the often-asymptomatic supraventricular extrasystole (SVE), pose significant challenges in early detection and precise diagnosis. These challenges are of paramount importance, as recurrent SVEs may elevate the risk of developing severe SVAs, potentially resulting in cardiac weakening and subsequent heart failure. In the study conducted, an innovative approach was introduced that combined a convolutional neural network (CNN) architecture to enable the early identification and characterization of SVEs within electrocardiogram (ECG) signals. The analysis leveraged a dataset comprising 78 half-hour recordings from the highly regarded MIT-BIH Arrhythmia Database, which included annotation headers serving as labels for each recording. Signals were down-sampled by a factor of 2 and split into windows of 512 samples, with 12,288 observations for training. Following the methodology, classic signal preprocessing techniques (filtering and data normalization) were used. The proposed model was based on the UNET 1D model. A binary cross-entropy loss function, Adam optimizer, and a batch size of 128 were obtained after a hyperparameter tuning. As a training-validation methodology, a 50-fold cross-validation technique was used. The approach demonstrated a Dice coefficient of 79.01%, a precision of 80.96%, and a recall rate of 86.60% in detecting SVE events. These findings were corroborated through meticulous comparison with the annotations provided by the MIT-BIH database. The results underscore the immense potential of CNN and deep learning techniques in the early detection of supraventricular arrhythmias. This approach not only offers a valuable tool for healthcare professionals engaged in telemonitoring and early intervention strategies but also represents a significant contribution to the field of cardiac health monitoring. By facilitating efficient and precise identification of SVEs, our research sets the stage for improved patient outcomes and the prevention of severe SVAs, marking substantial advancements in this critical domain. Full article

Atrial fibrillation (AF) is the most common persistent arrhythmia, and it is crucial to develop generalizable automatic AF detection methods. However, supervised AF detection is often limited in performance due to the difficulty in obtaining labeled data. To address the gap between limited labeled data and the requirements for model robustness and generalization in single-lead ECG AF detection, we proposed a semi-supervised contrastive learning method named MLMCL for AF detection. The MLMCL method utilizes the multi-level feature representations of the encoder to perform multiple contrastive learning to fully exploit temporal consistency, channel consistency, and label consistency. Meanwhile, it combines labeled and unlabeled data for pre-training to obtain robust features for downstream tasks. In addition, it uses the domain knowledge in the field of AF diagnosis for domain knowledge augmentation to generate hard samples and improve the distinguishability of ECG representations. In the cross-dataset testing mode, MLMCL had better performance and good stability on different test sets, demonstrating its effectiveness and robustness in the AF detection task. The comparison results with existing studies show that MLMCL outperformed existing methods in external tests. The MLMCL method can be extended and applied to multi-lead scenarios and has reference significance for the development of contrastive learning methods for other arrhythmia. Full article

With the increasing complexity of urban roads and rising traffic flow, traffic safety has become a critical societal concern. Current research primarily addresses drivers’ attention, reaction speed, and perceptual abilities, but comprehensive assessments of cognitive abilities in complex traffic environments are lacking. This study, grounded in cognitive science and neuropsychology, identifies and quantitatively evaluates ten cognitive components related to driving decision-making, execution, and psychological states by analyzing video footage of drivers’ actions. Physiological data (e.g., Electrocardiogram (ECG), Electrodermal Activity (EDA)) and non-physiological data (e.g., Eye Tracking (ET)) are collected from simulated driving scenarios. A dual-branch Transformer network model is developed to extract temporal features from multimodal data, integrating these features through a weight adjustment strategy to predict driving-related cognitive abilities. Experiments on a multimodal driving dataset from the Computational Physiology Laboratory at the University of Houston, USA, yield an Accuracy (ACC) of 0.9908 and an F1-score of 0.9832, confirming the model’s effectiveness. This method effectively combines scale measurements and driving behavior under secondary tasks to assess cognitive abilities, providing a novel approach for driving risk assessment and traffic safety strategy development. Full article

Background: Screening for cardiovascular disease (CVD) and its associated risk factors in childhood facilitates early detection and timely preventive interventions. However, limited data are available regarding screening tools and their diagnostic yield when applied in unselected pediatric populations. Aims: To evaluate the performance of a CVD screening program, based on history, 12-lead ECG and phonocardiography, applied in primary school children. Methods: The methods used were prospective study, with voluntary participation of third-grade primary school children in the region of Crete/Greece, over 6 years (2018–2024). Personal and family history were collected by using a standardized questionnaire and physical evaluation (including weight, height, blood pressure measurement), and cardiac auscultation (digital phonocardiography (PCG)) and 12-lead electrocardiogram (ECG) were recorded at local health stations (Phase I). Following expert verification of responses and obtained data, assisted by designated electronic health record with incorporated decision support algorithms (phase II), pediatric cardiology evaluation at the tertiary referral center followed (phase III). Results: A total of 944 children participated (boys 49.6%). A total of 790 (83.7%) had Phase I referral indication, confirmed in 311(32.9%) during Phase II evaluation. Adiposity (10.8%) and hypertension (3.2%) as risk factors for CVD were documented in 10.8% and 3.2% of the total population, respectively. During Phase III evaluations (n = 201), the majority (n = 132, 14% of total) of children were considered as having a further indication for evaluation by other pediatric subspecialties for their reported symptoms. Abnormal CVD findings were present in 69 (7.3%) of the study population, including minor/trivial structural heart disease in 23 (2.4%) and 17 (1.8%), respectively, referred due to abnormal cardiac auscultation, and ECG abnormalities in 29 (3%), of which 6 (0.6%) were considered potentially significant (including 1 case of genetically confirmed channelopathy-LQT syndrome). Conclusions: CVD screening programs in school children can be very helpful for the early detection of CVD risk factors and of their general health as well. Expert cardiac auscultation and 12-lead ECG allow for the detection of structural and arrhythmogenic heard disease, respectively. Further study is needed regarding performance of individual components, accuracy of interpretation (including computer assisted diagnosis) and cost-effectiveness, before large-scale application of CVD screening in unselected pediatric populations. Full article

Artificial intelligence (AI) is revolutionizing healthcare by offering innovative solutions for diagnosis, treatment, and patient management. Only recently has the field of pediatric cardiology begun to explore the use of deep learning methods to analyze electrocardiogram (ECG) data, aiming to enhance diagnostic accuracy, expedite workflows, and improve patient outcomes. This review examines the current state of AI-enhanced ECG interpretation in pediatric cardiology applications, drawing insights from adult AI-ECG research given the progress in this field. It describes a broad range of AI methodologies, investigates the unique challenges inherent in pediatric ECG analysis, reviews the current state of the literature in pediatric AI-ECG, and discusses potential future directions for research and clinical practice. While AI-ECG applications have demonstrated considerable promise, widespread clinical adoption necessitates further research, rigorous validation, and careful consideration of equity, ethical, legal, and practical challenges. Full article

Background: The fourth universal definition of myocardial infarction (MI) introduced the differentiation of acute myocardial injury from MI. In this study, we developed a computational phenotype for distinct identification of acute myocardial injury and MI within electronic medical records (EMRs). Methods: Two cohorts were used from a Calgary-wide EMR system: a chart review of 3042 randomly selected inpatients from Dec 2014 to Jun 2015; and 11,685 episodes of care that included cardiac catheterization from Jan 2013 to Apr 2017. Electrocardiogram (ECG) reports were processed using natural language processing and combined with high-sensitivity troponin lab results to classify patients as having an acute myocardial injury, MI, or neither. Results: For patients with an MI diagnosis, only 64.0% (65.7%) in the catheterized cohorts (chart review cohort) had two troponin measurements within 6 h of each other. For patients with two troponin measurements within 6 h; of those with an MI diagnosis, our phenotype classified 25.2% (31.3%) with an acute myocardial injury and 62.2% (55.2%) with an MI in the catheterized cohort (chart review cohort); and of those without an MI diagnosis, our phenotype classified 12.9% (12.4%) with an acute myocardial injury and 10.0% (13.1%) with an MI in the catheterized cohort (chart review cohort). Conclusions: Patients with two troponin measurements within 6 h, identified by our phenotype as having either an acute myocardial injury or MI, will at least meet the diagnostic criteria for an acute myocardial injury (barring lab errors) and indicate many previously uncaptured cases. Myocardial infarctions are harder to be certain of because ECG report findings might be superseded by evidence not included in our phenotype, or due to errors with the natural language processing. Full article

Electrocardiogram (ECG) signals contain complex and diverse features, serving as a crucial basis for arrhythmia diagnosis. The subtle differences in characteristics among various types of arrhythmias, coupled with class imbalance issues in datasets, often hinder existing models from effectively capturing key information within these complex signals, leading to a bias towards normal classes. To address these challenges, this paper proposes a method for arrhythmia classification based on a multi-branch, multi-head attention temporal convolutional network (MB-MHA-TCN). The model integrates three convolutional branch layers with different kernel sizes and dilation rates to capture features across varying temporal scales. A multi-head self-attention mechanism dynamically allocates weights, integrating features and correlations from different branches to enhance the recognition capability for difficult-to-classify samples. Additionally, the temporal convolutional network employs multi-layer dilated convolutions to progressively expand the receptive field for extracting long-term dependencies. To tackle data imbalance, a novel data augmentation strategy is implemented, and focal loss is utilized to increase the weight of minority classes, while Bayesian optimization is employed to fine-tune the model’s hyperparameters. The results from five-fold cross-validation on the MIT-BIH Arrhythmia Database demonstrate that the proposed method achieves an overall accuracy of 98.75%, precision of 96.60%, sensitivity of 97.21%, and F1 score of 96.89% across five categories of ECG signals. Compared to other studies, this method exhibits superior performance in arrhythmia classification, significantly improving the recognition rate of minority classes. Full article

Background: The wearable cardioverter defibrillator (WCD) has emerged as a valuable tool used for temporary protection from sudden cardiac death. However, since the WCD uses surface electrodes to detect arrhythmias, it is susceptible to inappropriate detection. Although shock conversion rates for the WCD are reported to be high for detected events, its efficacy in clinical practice tends to be degraded by patient noncompliance. Reasons for this include wearer discomfort and frequent false alarms, which may interrupt sleep and generate anxiety. Up to now, data on the incidence of false alarms emitted by the WCD and their predictors are rare. Objectives: The aim of our study was to assess the relationship between both artifact sensing and episode misclassification burden and wearing compliance in patients with a WCD (ZOLL LifeVest™ 4000 system, ZOLL CMS GmbH, Cologne, Germany). Methods and Results: We conducted a single-center retrospective observational study, analyzing patients with a WCD prescribed at our institution. A total of 134 patients (mean age 51.7 ± 13.8 years, 79.1% male) were included. Arrhythmia recordings were analyzed and categorized as non-sustained ventricular tachycardia, sustained ventricular tachycardia or fibrillation, artifact sensing or misclassified episodes. Indication for WCD prescription was both primary and secondary prophylaxis. A total of 3019 false WCD alarms were documented in 78 patients (average number of false alarms 38.7 ± 169.5 episodes per patient) over a mean WCD wearing time of 71.5 ± 70.9 days (daily WCD wearing time 20.2 ± 5.0 h). In a total of 78 patients (58.2% of the study population), either artifact sensing (76.9%), misclassified episodes (6.4%), or both (16.7%) occurred. Misclassified episodes included sinus tachycardias, atrial flutter, atrial fibrillation, premature ventricular contractions (PVCs), and intermittent bundle branch block. A multiple linear regression identified loop diuretics (regression coefficient [B] −0.11; 95% CI −0.21–(−0.0001); p = 0.049), angiotensin receptor–neprilysin inhibitors (ARNIs) (B −0.11; 95% CI 0.22–(−0.01); p = 0.033), and a higher R-amplitude of the WCD baseline electrocardiogram (ECG) (B −0.17; 95% CI −0.27–(−0.07); p = 0.001) as independent predictors for a lower number of artifact episodes per day. In addition, atrial fibrillation (B 0.05; 95% CI 0.01–0.08; p = 0.010), and calcium antagonists (B 0.07; 95% CI 0.02–0.12; p = 0.012) were independent predictors for increased numbers of misclassified episodes per day, while beta-blockers seemed to reduce them (B −0.06; 95% CI −0.10–(−0.01); p = 0.013). Patients terminated 61.0% of all false alarms manually by pressing the response button on average 1.9 times per false alarm with overall 3.6 manual terminations per affected patient per month. Conclusions: In conclusion, false alarms from the ZOLL LifeVest™ system were frequent, with artifact sensing being the most common cause. Hence, the occurrence of false alarms represents a significant side effect of WCD therapy, and efforts should be made to minimize false alarms. Full article

This study presents a novel driver authentication system utilizing electrocardiogram (ECG) signals collected through dry electrodes embedded in the steering wheel. Traditional biometric authentication methods are sensitive to environmental changes and vulnerable to replication, but this study addresses these issues by leveraging the unique characteristics and forgery resistance of ECG signals. The proposed system is designed using autocorrelation profiles (ACPs) and a convolutional neural network and is optimized for real-time processing even in constrained hardware environments. Additionally, advanced signal processing algorithms were applied to refine the ECG data and minimize noise in driving environments. The system’s performance was evaluated using a public dataset of 154 participants and a real-world dataset of 10 participants, achieving F1-Scores of 96.8% and 96.02%, respectively. Furthermore, an ablation study was conducted to analyze the importance of components such as ACPs, normalization, and filtering. When all components were removed, the F1-Score decreased to 60.1%, demonstrating the critical role of each component. These findings highlight the potential of the proposed system to deliver high accuracy and efficiency not only in vehicle environments but also in various security applications. Full article

This study investigates whether heart rate asymmetry (HRA) parameters offer insights into sleep stages beyond those provided by conventional heart rate variability (HRV) and complexity measures. Utilizing 31 polysomnographic recordings, we focused exclusively on electrocardiogram (ECG) data, specifically the RR interval time series, to explore heart rate dynamics associated with different sleep stages. Employing both statistical techniques and machine learning models, with the Generalized Estimating Equation model as the foundational approach, we assessed the effectiveness of HRA in identifying and differentiating sleep stages and transitions. The models including asymmetric variables for detecting deep sleep stages, N2 and N3, achieved AUCs of 0.85 and 0.89, respectively, those for transitions N2–R, R–N2, i.e., falling in and out of REM sleep, achieved AUCs of 0.85 and 0.80, and those for W–N1, i.e., falling asleep, an AUC of 0.83. All these models were highly statistically significant. The findings demonstrate that HRA parameters provide significant, independent information about sleep stages that is not captured by HRV and complexity measures alone. This additional insight into sleep physiology potentially leads to a better understanding of hearth rhythm during sleep and devising more precise diagnostic tools, including cheap portable devices, for identifying sleep-related disorders. Full article

Security is one of the increasingly significant issues given advancements in technology that harness data from multiple devices such as the internet of medical devices. While protecting data from unauthorized user access, several techniques are used including fingerprints, passwords, and others. One of the techniques that has attracted much attention is the use of human features, which has proven to be most effective because of the difficulties in impersonating human-related features. An example of a human-related attribute includes the electrical signal generated from the heart, mostly referred to as an Electrocardiogram (ECG) signal. The methods to extract features from ECG signals are time domain-based; however, the challenge with relying only on the time-domain or frequency-domain method is the inability to capture the intra-leading relationship of Variational Mode Decomposition signals. In this research, fusing multiple domains ECG feature and adaptive Variational Mode Decomposition approaches are utilized to mitigate the challenge of losing the intra-leading correlations of mode decompositions, which might reduce the robustness of encryption algorithms. The features extracted using the reconstructed signal have a mean (0.0004), standard deviation (0.0391), skewness (0.1562), and kurtosis (1.2205). Among the lightweight encryption methods considered, Chacha20 has a total execution time of 27µs. The study proposes a lightweight encryption technique based on the fused vector representation of extracted features to provide an encryption scheme in addition to a bio-inspired key generation technique for data encryption. Full article

A battery-operated biomedical wearable device gradually assists in clinical tasks to monitor patients’ health states regarding early diagnosis and detection. This paper presents the development of a self-powered portable electronic module by integrating an onboard energy-harvesting facility for electrocardiogram (ECG) signal processing and personalized health monitoring. The developed electronic module provides a customizable approach to power the device using a lithium-ion battery, a series of silicon photodiode arrays, and a solar panel. The new architecture and techniques offered by the developed method include an analog front-end unit, a signal processing unit, and a battery management unit for the acquiring and processing of real-time ECG signals. The dynamic multi-level wavelet packet decomposition framework has been used and applied to an ECG signal to extract the desired features by removing overlapped and repeated samples from an ECG signal. Further, a random forest with deep decision tree (RFDDT) architecture has been designed for offline ECG signal classification, and experimental results provide the highest accuracy of 99.72%. One assesses the custom-developed sensor by comparing its data with those of conventional biosensors. The onboard energy-harvesting and battery management circuits are designed with a BQ25505 microprocessor with the support of silicon photodiodes and solar cells which detect the ambient light variations and provide a maximum of 4.2 V supply to enable the continuous operation of an entire module. The measurements conducted on each unit of the proposed method demonstrate that the proposed signal-processing method significantly reduces the overlapping samples from the raw ECG data and the timing requirement criteria for personalized and wearable health monitoring. Also, it improves temporal requirements for ECG data processing while achieving excellent classification performance at a low computing cost. Full article

Background/Objectives: Routine screening electrocardiograms (ECGs) prior to starting medications for attention-deficit/hyperactivity disorder (ADHD) remain controversial. This real-world study assessed corrected QT (QTc) interval data from pediatric patients who had a baseline ECG performed prior to initiating treatment with ADHD medications and ≥6 months of clinical follow-up. Methods: A retrospective chart review of children aged 2–18 years diagnosed with ADHD with/without autism spectrum disorder (ASD) at child neurology clinics in Jordan (June 2019 and June 2021) was performed, and children were prescribed with ADHD medications to manage symptoms. Patients had ≥6 months of follow-up and no known cardiac disease/family history. A baseline ECG and regular clinical exams were performed for each child. Results: Of 458 patients with baseline ECGs, 362 met the study inclusion criteria. Overall, 286 (79.0%) patients were diagnosed with ASD/comorbid ADHD and 76 (21.0%) with ADHD alone; 61 (16.9%) were prescribed atomoxetine, 38 (10.5%) methylphenidate, 134 (37.0%) risperidone, and 129 (35.6%) aripiprazole. The patients’ mean ± SD age was 6.4 ± 3.5 years, and most were male (n = 268, 74.0%). The mean baseline QTc interval was 400 ± 22 ms (median, 400 ms); one patient had a QTc interval >460 ms and was excluded from initiating treatment with any ADHD medications. During the ≥6-month follow-up, none of the patients had any signs or symptoms of adverse cardiac effects. Conclusions: Routine screening ECGs prior to treatment with ADHD medications may not be necessary in healthy children with no family history of cardiac disease. However, further studies are needed to evaluate the long-term effects of ADHD medications in low-risk pediatric patients. Full article